Share Email Print
cover

Proceedings Paper

Deep neural networks for low-dose CT image reconstruction via cooperative meta-learning strategy
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Recently, deep neural networks (DNNs) have been widely applied in low-dose computed tomography (LDCT) imaging field. Their performances are highly related to the number of the pre-collected training data. Meanwhile, the training data is usually hard to obtain, especially for the high-dose CT (HDCT) images. And HDCT images sometimes contain undesired noises, which easily result in network overfitting. To address the two issues, we proposed a cooperative meta-learning strategy for CT image reconstruction (CmetaCT) combining the metalearning strategy and Co-teaching strategy. The meta-learning (teacher/student model) strategy allows for training network with a large number of LDCT images without the corresponding HDCT images and only a small number of labeled CT data in a semi-supervised learning manner. And the Co-teaching strategy is able to make a trade-off between overfitting and introducing extra errors, which includes a part of samples in every minibatch for updating model parameters. Due to the capacity of meta-learning, the presented CmetaCT method is flexible enough to utilize any existing CT restoration/reconstruction network in meta-learning framework. Finally, both quantitative and visual results indicated that the proposed CmetaCT method achieves a superior performance on low-dose CT imaging compared with the DnCNN method.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131243 (16 March 2020); doi: 10.1117/12.2548950
Show Author Affiliations
Manman Zhu, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Sui Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Danyang Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Qi Gao, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Shanli Zhang, The First Affiliated Hospital of Guangzhou Univ. of Traditional Chinese Medicine (China)
Haiyun Huang, South China Univ. of Technology (China)
Zhaoying Bian, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Jing Huang, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Dong Zeng, South China Univ. of Technology (China)
Jianhua Ma, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray